我在调试张量流代码时遇到问题，无法找到nan值的来源。我修改了pix2pix代码，以便l1现在丢失计算像素之间的cie94颜色距离：
with tf.name_scope("generator_loss"):
# predict_fake => 1
# abs(targets - outputs) => 0
kl = 1.0
k1 = 0.045
k2 = 0.015
targetslab = rgb_to_lab(targets)
outputslab = rgb_to_lab(outputs)
targets_l, targets_a, targets_b = preprocess_lab(targetslab)
outputs_l, outputs_a, outputs_b = preprocess_lab(outputslab)
euclidean = tf.sqrt(tf.square(targets_l-outputs_l) + tf.square(targets_a-outputs_a) + tf.square(targets_b-outputs_b))
delta_l = tf.abs(targets_l - outputs_l)
c1 = tf.sqrt(tf.square(targets_a) + tf.square(targets_b))
c2 = tf.sqrt(tf.square(outputs_a) + tf.square(outputs_b))
delta_c = tf.abs(c1 - c2)
delta_h = tf.sqrt(tf.square(euclidean) - tf.square(delta_l) - tf.square(delta_c))
#delta_a = tf.abs(targets_a - outputs_a)
#delta_b = tf.abs(targets_b - outputs_b)
sl = 1.0
sc = 1.0 + tf.multiply(k1, c1)
sh = 1.0 + tf.multiply(k2, c1)
gen_loss_L1 = tf.reduce_mean(tf.sqrt(tf.square(tf.truediv(delta_l, tf.multiply(kl, sl))
+ tf.truediv(delta_c, sc) + tf.truediv(delta_h, sh))))
gen_loss_GAN = tf.reduce_mean(-tf.log(predict_fake + EPS))
#gen_loss_L1 = tf.reduce_mean(tf.abs(targets - outputs))
gen_loss = gen_loss_GAN * a.gan_weight + gen_loss_L1 * a.l1_weight
从l1更改为我的自定义损失函数后，我在输出中不断获得nan值。之前一切都很好，所以我绝对没有目标/输出中的nan值。